Lightweight Automotive Materials And Structural Design and Performance Prediction Based on Fundamental Algorithms

Abstract

In the context of global energy conservation, emission reduction, and sustainable development, automotive lightweight technology has become a crucial direction for the transformation and upgrading of the automotive industry. By reducing vehicle weight, it is possible to significantly improve fuel economy, extend the driving range of new energy vehicles, and decrease overall greenhouse gas emissions, thereby supporting both environmental and economic goals. This paper centers on the design and performance prediction of lightweight automotive materials and structures, with a particular emphasis on the integration of advanced algorithms into engineering practice. It first reviews the industrial background and strategic significance of lightweighting, followed by a summary of the mechanical, thermal, and economic properties of common lightweight materials such as aluminum alloys, magnesium alloys, high-strength steels, and composite materials. Furthermore, the paper introduces collaborative design concepts that couple material development with structural innovation, and discusses multiple lightweight design strategies. Special attention is given to the role of machine learning, topology optimization, and multi-objective optimization in predicting material performance, guiding structural design, and balancing trade-offs among cost, safety, and sustainability. Finally, through case studies of typical manufacturers, the paper analyzes practical applications and summarizes future trends, aiming to provide a comprehensive theoretical reference and methodological guidance for research and practice in automotive lightweight technology.